Next Generation Algorithms for Planning Production and Inventories with Uncertain Demand and Congestion

该奖项为考虑不确定需求、使用随机优化工具、考虑产能有限的生产资源的排队行为（以非线性清除函数为特征）的综合、计算易于处理和用户友好的生产计划算法的开发提供资金。在当今的全球供应链中，跨空间和时间的有效协调对于半导体制造业等资本密集型行业至关重要，在这些行业中，全球供应链、短产品生命周期和快速变化的市场条件使得有效的供应链协调至关重要。然而，尽管与生产计划和库存有关的问题在过去五十年来一直是工业工程和业务研究的常见问题，但对工业面临的问题仍然没有全面的解决办法。该项目的具体目标是为更大的问题开发精确解方法和近似解，这些问题的解质量可以通过分析边界或系统计算实验来表征。该项目的试验台将是半导体行业，其全球供应链，短的产品生命周期和快速变化的市场条件使得有效的供应链协调至关重要。如果成功，这项工作对美国工业竞争力的影响将是巨大的。支持供应链协调的公司范围的计划模型已经多次被证明能够产生显著的效益，并且以前在数学规划和随机库存建模领域单独研究的概念的集成将为生产计划的重大新发展提供途径。在教育方面也有显著的好处。大多数IE/OR课程将确定性建模和随机建模作为两个完全不同的课程序列，其结果是学生认为这两种技术无关。本研究的工作将在本科阶段教授，帮助学生更深入地理解确定性模型和随机模型之间的联系。

This award provides funding for the development of comprehensive, computationally tractable and user-friendly production planning algorithms that consider uncertain demand, using the tools of stochastic optimization, and the queuing behavior that characterizes production resources with limited capacity, characterized by nonlinear clearing functions. In today's global supply chains, effective coordination of operations across space and time is vital to capital-intensive industries like semiconductor manufacturing where global supply chains, short product life cycles and rapidly changing market conditions render effective supply chain coordination critical. However, despite the fact that problems related to the planning of production and inventories have been the stock in trade of industrial engineering and operations research for the last five decades, a comprehensive solution to the problem as faced in industry is still unavailable. The specific aim of this project is to develop both exact solution methods as well as approximate solutions for larger problems whose solution quality can by characterized either through analytical bounds or by systematic computational experiments. The test bed for this project will be the semiconductor industry, whose global supply chains, short product life cycles and rapidly changing market conditions render effective supply chain coordination critical.If successful, the impact of this work on U.S. industrial competitiveness will be significant. Company-wide planning models supporting supply chain coordination have repeatedly been shown to yield significant benefits, and the integration of concepts that have previously been studied separately in the domains of mathematical programming and stochastic inventory modeling will provide an avenue for significant new developments in production planning. In terms of education there are also significant benefits. Most IE/OR curricula present deterministic and stochastic modeling as two completely distinct course sequences, with the result that students view the two technologies as unrelated. The work developed in this research will be taught at the undergraduate level, helping students develop a deeper understanding of the connection between deterministic and stochastic models.